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Emotion Recognition from Physiological Channels Using Graph Neural Network

In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work...

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Autores principales: Wierciński, Tomasz, Rock, Mateusz, Zwierzycki, Robert, Zawadzka, Teresa, Zawadzki, Michał
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025566/
https://www.ncbi.nlm.nih.gov/pubmed/35458965
http://dx.doi.org/10.3390/s22082980
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author Wierciński, Tomasz
Rock, Mateusz
Zwierzycki, Robert
Zawadzka, Teresa
Zawadzki, Michał
author_facet Wierciński, Tomasz
Rock, Mateusz
Zwierzycki, Robert
Zawadzka, Teresa
Zawadzki, Michał
author_sort Wierciński, Tomasz
collection PubMed
description In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman’s model while the accuracy of the Circumplex model is similar to the baseline methods.
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spelling pubmed-90255662022-04-23 Emotion Recognition from Physiological Channels Using Graph Neural Network Wierciński, Tomasz Rock, Mateusz Zwierzycki, Robert Zawadzka, Teresa Zawadzki, Michał Sensors (Basel) Article In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman’s model while the accuracy of the Circumplex model is similar to the baseline methods. MDPI 2022-04-13 /pmc/articles/PMC9025566/ /pubmed/35458965 http://dx.doi.org/10.3390/s22082980 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wierciński, Tomasz
Rock, Mateusz
Zwierzycki, Robert
Zawadzka, Teresa
Zawadzki, Michał
Emotion Recognition from Physiological Channels Using Graph Neural Network
title Emotion Recognition from Physiological Channels Using Graph Neural Network
title_full Emotion Recognition from Physiological Channels Using Graph Neural Network
title_fullStr Emotion Recognition from Physiological Channels Using Graph Neural Network
title_full_unstemmed Emotion Recognition from Physiological Channels Using Graph Neural Network
title_short Emotion Recognition from Physiological Channels Using Graph Neural Network
title_sort emotion recognition from physiological channels using graph neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025566/
https://www.ncbi.nlm.nih.gov/pubmed/35458965
http://dx.doi.org/10.3390/s22082980
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